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A target tracking method based on adaptive occlusion judgment and model updating strategy.

Zhiming Cai1,2, Zhuangzhuang Wang1, Jianchao Huang1

  • 1School of Electronic, Electrical Engineering and Physics, Fujian University of Technology, Fuzhou, Fujian, China.

Peerj. Computer Science
|December 11, 2023
PubMed
Summary

This study introduces Aojmus, an advanced kernel correlation filter tracker that enhances target tracking robustness. It effectively handles occlusion and motion blur, improving overall performance in computer vision applications.

Keywords:
Adaptive occlusion judgmentDouble thresholdsFour evaluation indicatorsModel updatingTarget tracking

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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Target tracking is crucial in computer vision but faces challenges with occlusion and motion blur.
  • Existing algorithms struggle to balance performance under varying environmental conditions.

Purpose of the Study:

  • To develop a robust target tracking algorithm that overcomes limitations of current methods.
  • To improve tracking accuracy and success rate, especially during target occlusion and motion blur.

Main Methods:

  • Proposed an improved kernel correlation filter algorithm (Aojmus) with adaptive occlusion judgment and model updating.
  • Fused Color-Naming (CN) and Histogram of Gradients (HOG) features with a scale filter for robust feature extraction.
  • Implemented a novel occlusion detection mechanism using four indicators and a double thresholding strategy.

Main Results:

  • Aojmus demonstrated superior tracking precision compared to four other algorithms on the OTB-2015 dataset.
  • Achieved excellent success rates, particularly in scenarios involving target occlusion and motion blur.
  • Exhibited real-time performance with a processing speed of 74.85 fps.

Conclusions:

  • The Aojmus algorithm offers a robust and efficient solution for target tracking.
  • Adaptive occlusion judgment and model updating significantly enhance tracking performance.
  • The proposed method provides a strong foundation for real-world computer vision applications requiring reliable tracking.